Artificial intelligence (AI) deals with the science of making intelligent machines. AI covers areas including cognition, understanding, learning, knowledge representation, and searching. The idea of making a machine with intelligence has existed since at least the 1940's when the first computers were made. Many predictions have been made since then as to when an intelligent machine will be created.
The field of AI has gone through several periods when great breakthroughs were thought to be close at hand. However, each time more barriers were found that frustrated the goal of creating an intelligent machine. These barriers included the exponential growth of the search space, consequent slowness in the search process, inability to generalize knowledge, and encoding and storing knowledge in a useful and efficient way. These problems apply to the AI field in general.
Any machine that can accomplish its specific task in the presence of uncertainty and variability in its environment is generally regarded as an intelligent machine. The machine's ability to monitor its environment, allowing it to adjust its actions based on what it has sensed, is a prerequisite for intelligence. For the purpose of this disclosure, we assume this definition. Examples of minimally-intelligent machines include industrial robots equipped with sensors, computers equipped with speech recognition and voice synthesis, self-guided vehicles relying on vision rather than on marked roadways, and so-called smart weapons, which are capable of target identification. These varied systems include three major subsystems such as sensors, actuators, and control.
Since the physical embodiment of the machine or the particular task performed by the machine does not mark it as intelligent, the appearance of intelligence must come from the nature of the control or decision-making process that the machine performs. Given the centrality of control to any form of intelligent machine, intelligent control is the essence of an intelligent machine.
Artificial neural networks are systems composed of many nonlinear computational elements operating in parallel and arranged in patterns reminiscent of biological neural nets. The computational elements, or nodes, are connected via variable weights that are typically adapted during use to improve performance. Thus, in solving a problem, neural net models can explore many competing hypothesis simultaneously using massively parallel nets composed of many computational elements connected by links with variable weights.
In a neural network, “neuron-like” nodes can output a signal based on the sum of their input currents, the output being the result of an activation function. In a neural network, there exists a plurality of connections, which electrically couple a plurality of neurons. The connections serve duel functions of communication bridges and computational configuration and represent a synthesis of memory and processing. A network of such “neuron-like” nodes has the ability to process information in a variety of useful ways.
Neural networks that have been developed till date are largely software-based. A true physical neural network (e.g., the human brain) is massively parallel (and therefore very fast in computation), very adaptable, and extremely low power. For example, half of a human brain can suffer a lesion early in its development and not seriously affect its performance, it consumes only 10 watts, and is arguably the most intelligent artifact in existence. Software simulations are slow because a serial computer must calculate connection strengths. When the networks get larger (and therefore more powerful and potentially useful), the computational time and power consumption becomes enormous.
The implementation of neural network systems has lagged behind their theoretical potential due to the difficulties in building physical neural network hardware. This is primarily because of the large numbers of neurons and weighted connections required. The emulation of even of the simplest biological nervous systems would require neurons and connections numbering in the millions. Due to the difficulties in building such highly interconnected and adaptive structures, the currently available neural network hardware systems have not approached this level of complexity. Another disadvantage of hardware systems is that they typically are often custom designed and built to implement one particular neural network architecture and are not easily, if at all, reconfigurable to implement different architectures. A true physical neural network chip, for example, has not yet been designed and successfully implemented.
Therefore, a need exists for a new type of compact computing architecture that contains electronics unlike anything currently in production. Also, the new architecture should add intelligences to the machines and allow the users to create adaptive autonomous agents, in real or virtual worlds.